Computers are often used to solve complex quantitative and qualitative problems. For certain types of problems, advanced computing techniques, such as genetic algorithms, may be available to develop a model, such as a neural network, that is used to solve the problem. However, genetic algorithms may take a large number of iterations to converge on an acceptable neural network.
Furthermore, various types of machine-learning problems exist. For example, regression problems involve evaluating a series of inputs to predict a numeric output, classification problems involve evaluating a series of inputs to predict a categorical output, and reinforcement learning involves performing actions within an environment while learning from feedback from the actions. Due to the differences in the various types of problems, the available mechanisms to generate and train a neural network may be problem-specific. For example, a method of generating and training a neural network to solve a regression problem may be significantly less efficient for generating and training a neural network to solve a classification problem